The present invention relates to machine learning based classification of vascular branches in medical images, and more particularly, to machine learning based classification of vascular branches for automated extraction and pruning of vascular centerlines in medical images.
Automatic segmentation of coronary arteries in Computed Tomography Angiography (CTA) facilitates the diagnosis, treatment, and monitoring of coronary artery diseases. An important step in coronary artery segmentation is to extract a curve along the center of the coronary artery referred to as a centerline. A centerline representation is important for the visualization of the artery through a curved planar reformatting. A centerline is also useful to support lumen segmentation methods for quantitative assessments, such as stenosis grading or CT based Fractional Flow Reserve (FFR) measurements. Methods have been proposed to extract coronary artery centerlines with very high sensitivity, but at the expense of detecting false branches that commonly leak into nearby structures, such as heart tissues, veins, or other bright structures in CTA images. Interactive editing tools are available for manually removing false centerlines from detections, but manually removing false centerlines using such interactive editing tools is often time consuming. Accordingly, automated post-processing tools to correct detected centerlines for accurate visualization and quantification purposes are desirable.